Beta Regression

Introduction

• Regression analysis is a statistical tool used to explore relationships between variables.

• Beta Regression: When the dependent variable is a ratio or percentage, constrained between 0 and 1.

Why not Linear Regression?

• It can predict values outside the range of 0 to 1.
• It assumes constant variance, which is not typical for bounded data.

Key Assumptions

• Beta distribution: Assumes the outcome follows a beta distribution, which is flexible for variables limited to (0, 1).

• Precision Parameter (\phi): Allows control over the variance of the outcome, enabling flexibility for data with differing levels of dispersion.

Beta distribution

The PDF of random variable with a beta distribution is as follows.

f(y) = \begin{cases} \frac{y^{\alpha-1}(1-y)^{\beta-1}}{B(\alpha,\beta)}, & 0 \le y \le 1 \\ 0, & \text{elsewhere} \end{cases} Where B(\alpha,\beta) = \int_0^1 y^{\alpha-1}(1-y)^{\beta-1} \ dy = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha+\beta)}.

\alpha and \beta are the shape variables where \alpha > 0 \quad \beta > 0. [1]

Beta Distribution Mean and Variance

E[Y] = \mu = \frac{\alpha}{\alpha+\beta} \ \ \ \text{and} \ \ \ V[Y] = \sigma^2 = \frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)} [1]

Introduction of \mu and \phi

For beta regression, it is useful to introduce the following

\mu = \frac{\alpha}{\alpha+\beta} \\ \phi = \alpha + \beta \mu is the mean of the beta regression while the higher the \phi the less the variance or the less spread out the PDF function is. [2]

Revised Beta Distribution

f(y; \mu, \phi) = \frac{\Gamma(\phi)}{\Gamma(\mu \phi) \Gamma((1 - \mu)\phi)} y^{\mu\phi - 1}(1 - y)^{(1 - \mu)\phi - 1},

\quad 0 < y < 1 Where:

• (μ) is the mean, (ϕ) is the precision (inverse of the variance), (Γ) is the gamma function.

Beta Distribution Variance

\text{Var}(Y) = \frac{\mu(1 - \mu)}{1 + \phi} When \mu is near the extremes, 0 or 1, variance drops. [3]

Extended Beta Regression

Bias Correction/Reduction - Type of Estimator:

• ML (Maximum Likelihood): Standard method, useful but may yield biased estimates in certain conditions.[4]

• BC (Bias-Corrected): Adjusts estimates to correct for bias, providing more reliable parameter values.

• BR (Bias-Reduced): Shrinks estimates towards a central value, which can improve predictive performance.

Extended Beta Regression

Beta Regression Trees

• This extension uses recursive partitioning to model data that might exhibit subgroup-specific relationships.

• It builds decision trees by splitting data into different subgroups based on the instability of model parameters across partitioning variables.

Methods - Suicide Rates Dataset

Model Approach:

• Beta Regression was used to model suicide rates as a function of socio-economic factors, appropriate for data bounded between 0 and 1.

• Dataset: Suicide Rates Overview 1985 to 2016, with variables like HDI, GDP per capita, sex, age group, and generation.

• Cleaned data by removing outliers using Cook’s distance and leverage analysis.

• Managed missing values and calculated descriptive statistics.

Methods Cont’d

Model Details:

• Incorporated interaction terms and adjusted precision (phi) to account for variance differences across groups.

• Used beta regression trees to capture nonlinear relationships.

Evaluation:

• Model performance assessed via pseudo R-squared.

• Software: R for data management and analysis.

Methods - Reading Skills Dataset

Model Rationale:

• ReadingSkills dataset (N=44): Examines reading scores (0.0–1.0) for 44 children, including 19 with dyslexia and 25 without.

• Beta regression models the response variable within the (0, 1) range, which suits the bounded reading scores better than normal regression.

Methods Cont’d

Data transformation:

• The response variable is scaled to (0, 1) and transformed using the logit function. The precision parameter (ϕ) is log-transformed and may vary by predictors like IQ and dyslexia status.

Analysis - Suicide Rates Dataset

Extended Beta Regression - Bias Correction

pending

Analysis Cont’d - Extended Beta Regression

Beta Regression Trees

The beta regression tree shows HDI_year as a key predictor, with specific thresholds creating groupings where higher HDI_year values link to better outcomes and smaller nodes show more variability.

Beta Regression Tree - Suicide Rates Dataset

Analysis Cont’d

Model Diagnostics

The package betareg allows users to perform both fixed and variable dispersion beta regression. The model is based on the beta distribution, using a parameterization with the mean and precision[5].

(add plots)

Analysis Cont’d

(graph)

Analysis - Reading Skills Dataset

Regressors Dataset Tweaking

• IQ (Z-score) - Dyslexia
  • Min -1.745 - No -> 0.0
  • Median -0.122 - Yes -> 1.0
  • Max 1.856 - Reading Score
• Dyslexia - 1.0 -> 0.99
  • Yes
  • No

Remember dependent variable is in open interval (0, 1)

Analysis Cont’d

Beta Regression Fitting

betareg(
  formula = accuracy ~ dcode * iq | dcode + iq,
  data = ReadingSkillsModel,
  type = "BC",
)

• BC is Bias Correction General Linear Regression

glm(
  formula = accuracy ~ dcode * iq,
  family = gaussian(link = "logit"), 
  data = ReadingSkillsModel,
)

• logit maps (0, 1) to \mathbb{R}

Analysis Cont’d

Results for Normal Children

Analysis Cont’d

Results for Dyslexic Children

Results - Suicide Dataset

Pending

Results - Reading Skills Dataset

Table 2

Results Cont’d

Dyslexia’s effect on scores

A child’s odds of answering a reading skills question correctly decreases by a factor of e^{1.446} if they are dyslexic assuming normal IQ.

Results Cont’d

IQ’s effect on scores

• If a normal child’s IQ increases by 1 standard deviation, their odds of answering a reading skills question correctly increases by a factor of e^{1.049}

• If a dyslexic child’s IQ increases by 1 standard deviation, their odds of answering a reading skills question correctly decreases by a factor of e^{0.095}

-0.095 = 1.049 - 1.144

Conclusion

• Effective for proportion data, Ideal for modeling data bounded in the (0, 1) range.
• Models both mean and precision, managing boundary cases and latent heterogeneity.
• Bias correction and beta regression trees expand its capabilities.
• The betareg package in R offers a powerful, flexible framework for analysts.